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Python Training for Scientists and Engineers

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Course duration

  • 5 days

Course Benefits

  • Create and run basic Python programs.
  • Design and code modules and classes.
  • Implement and run unit tests.
  • Use benchmarks and profiling to speed up programs.
  • Work with various data formats.
  • Process XML and JSON.
  • Manipulate arrays with NumPy.
  • Get a grasp of the diversity of subpackages that make up SciPy.
  • Leverage pandas to easily create and structure data.
  • Use matplotlib to create amazing visualizations. 
  • Use Jupyter notebooks for ad hoc calculations, plots, and what-if?.

Course Outline

  1. The Python Environment
    1. Starting Python
    2. Using the Interpreter
    3. Running a Python Script
    4. Python Scripts on Unix
    5. Python Scripts on Windows
    6. Python Editors and IDEs
  2. Getting Started
    1. Using Variables
    2. Built-in Functions
    3. Strings
    4. Numbers
    5. Converting among Types
    6. Writing to the Screen
    7. String Formatting
    8. Command Line Parameters
  3. Flow Control
    1. About Flow Control
    2. What's with the White Space?
    3. if and else
    4. Conditional Expressions
    5. Relational Operators
    6. Boolean Operators
    7. while Loops
    8. Alternate Ways to Exit a Loop
  4. Lists and Tuples
    1. About Sequences
    2. Lists
    3. Tuples
    4. Indexing and Slicing
    5. Iterating through a Sequence
    6. Functions for All Sequences
    7. Operators and Keywords for Sequences
    8. Nested Sequences
    9. List Comprehensions
    10. Generator Expressions
  5. Working with Files
    1. Text file I/O
    2. Opening a Text File
    3. Reading a Text File
    4. Writing to a Text File
    5. "Binary" (Raw, or Non-delimited) Data
  6. Dictionaries and Sets
    1. About Dictionaries
    2. When to Use Dictionaries
    3. Creating Dictionaries
    4. Iterating through a Dictionary
    5. About Sets
    6. Creating Sets
    7. Working with Sets
  7. Functions
    1. Defining a Function
    2. Function Parameters
    3. Variable Scope
    4. Returning Values
    5. Lambda Functions
  8. Exception Handling
    1. Syntax Errors
    2. Exceptions
    3. Handling Exceptions with Try
    4. Handling Multiple Exceptions
    5. Handling Generic Exceptions
    6. Ignoring Exceptions
    7. Using else
    8. Cleaning Up with finally
    9. Re-raising Exceptions
    10. Raising a New Exception
  9. OS Services
    1. The os Module
    2. Environment Variables
    3. Launching External Commands
    4. Paths, Directories, and Filenames
    5. Walking Directory Trees
    6. Dates and Times
  10. Modules and Packages
    1. Initialization code
    2. Namespaces
    3. Executing modules as scripts
    4. Documentation
    5. Packages and name resolution
    6. Naming conventions
    7. Using imports
  11. Classes
    1. Defining Classes
    2. Constructors
    3. Instance methods and data
    4. Attributes
    5. Inheritance
    6. Multiple Inheritance
  12. Programmer Tools
    1. Program Development
    2. Comments
    3. pylint
    4. Customizing pylint
    5. Unit Testing
    6. The unittest Module
    7. Creating a Test Class
    8. Establishing Success or Failure
    9. Startup and Cleanup
    10. Running the Tests
    11. Debugging
    12. Benchmarking
    13. Profiling Applications
  13. Excel Spreadsheets
    1. openpyxl module
    2. Reading an Existing Spreadsheet
    3. Creating a Spreadsheet
    4. Modifying a Spreadsheet
  14. XML and JSON
    1. Creating XML Files
    2. Parsing XML
    3. Tags and XPath
    4. Reading and Wiritng JSON
  15. iPython and Jupyter
    1. About iPython and Jupyter
    2. iPython Basics
    3. Jupyter Basics
  16. NumPy
    1. Python's scientific Stack
    2. NumPy Overview
    3. Creating Arrays
    4. Creating Ranges
    5. Working with Arrays
    6. Shapes
    7. Slicing and Indexing
    8. Indexing with booleans
    9. Stacking
    10. Iterating
    11. Tricks with Arrays
    12. Matrices
    13. Data Types
    14. NumPy Functions
  17. SciPy
    1. About SciPy
    2. SciPy Packages
    3. SciPy Examples
  18. pandas
    1. About pandas
    2. Series
    3. DataFrames
    4. Reading and Writing Data
    5. Indexing and Slicing
    6. Merging and Joining Data Sets
  19. matplotlib
    1. Creating a plot
    2. Commonly Used Plots
    3. Customizing Styles
    4. Ad hoc data visualization
    5. Advanced Usage
    6. Saving Images

Class Materials

Each student will receive a comprehensive set of materials, including course notes and all the class examples.

Class Prerequisites

Experience in the following would be useful for this Python class:

  • While there are no programming prerequisites, programming experience is helpful. Students should be comfortable working with files and folders, and should not be afraid of the command line.
Since its founding in 1995, InterSource has been providing high quality and highly customized training solutions to clients worldwide. With over 500 course titles constantly updated and numerous course customization and creation possibilities, we have the capability to meet your I.T. training needs.
Instructor-led courses are offered via a live Web connection, at client sites throughout Europe, and at our Geneva Training Center.
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